Infrared thermal imaging-based crack detection using deep learning J Yang, W Wang, G Lin, Q Li, Y Sun, Y Sun Ieee Access 7, 182060-182077, 2019 | 115 | 2019 |
Machine learning regression guided thermoelectric materials discovery–a review G Han, Y Sun, Y Feng, G Lin, N Lu ES Materials & Manufacturing 14 (18), 20-35, 2021 | 35 | 2021 |
Predicting mechanical properties from microstructure images in fiber-reinforced polymers using convolutional neural networks Y Sun, I Hanhan, MD Sangid, G Lin arXiv preprint arXiv:2010.03675, 2020 | 28 | 2020 |
Probabilistic state estimation approach for AC/MTDC distribution system using deep belief network with non-Gaussian uncertainties Y Huang, Q Xu, C Hu, Y Sun, G Lin IEEE Sensors Journal 19 (20), 9422-9430, 2019 | 24 | 2019 |
Local feature sufficiency exploration for predicting security-constrained generation dispatch in multi-area power systems Y Sun, X Fan, Q Huang, X Li, R Huang, T Yin, G Lin 2018 17th IEEE International Conference on Machine Learning and Applications …, 2018 | 16 | 2018 |
Deepgraphonet: A deep graph operator network to learn and zero-shot transfer the dynamic response of networked systems Y Sun, C Moya, G Lin, M Yue IEEE Systems Journal, 2023 | 12 | 2023 |
Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity Z Guo, P Roy Chowdhury, Z Han, Y Sun, D Feng, G Lin, X Ruan npj Computational Materials 9 (1), 95, 2023 | 12 | 2023 |
A data-centric weak supervised learning for highway traffic incident detection Y Sun, T Mallick, P Balaprakash, J Macfarlane Accident Analysis & Prevention 176, 106779, 2022 | 11 | 2022 |
Artificial intelligence guided thermoelectric materials design and discovery G Han, Y Sun, Y Feng, G Lin, N Lu Advanced Electronic Materials 9 (8), 2300042, 2023 | 7* | 2023 |
Effective risk prediction of tailings ponds using machine learning J Yang, Y Sun, Q Li, Y Sun 2020 3rd International Conference on Advanced Electronic Materials …, 2020 | 7 | 2020 |
Vapor–liquid equilibrium estimation of n-alkane/nitrogen mixtures using neural networks S Chakraborty, Y Sun, G Lin, L Qiao Journal of Computational and Applied Mathematics 408, 114059, 2022 | 5 | 2022 |
Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives R Egele, T Chang, Y Sun, V Vishwanath, P Balaprakash arXiv preprint arXiv:2309.14936, 2023 | 4 | 2023 |
Artificial intelligence inferred microstructural properties from voltage–capacity curves Y Sun, S Mitra Ayalasomayajula, A Deva, G Lin, RE García Scientific Reports 12 (1), 13421, 2022 | 4 | 2022 |
Deep neural network regression and sobol sensitivity analysis for daily solar energy prediction given weather data Y Sun Purdue University, 2018 | 4 | 2018 |
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization? R Egele, I Guyon, Y Sun, P Balaprakash arXiv preprint arXiv:2307.15422, 2023 | 3 | 2023 |
Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models Y Sun, E Cucuzzella, S Brus, SHK Narayanan, B Nadiga, L Van Roekel, ... NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning, 2023 | 1 | 2023 |
Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates Y Sun, E Cucuzzella, S Brus, SHK Narayanan, B Nadiga, L Van Roekel, ... Proceedings of the Platform for Advanced Scientific Computing Conference, 1-10, 2024 | | 2024 |
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach Y Sun, O Sowunmi, R Egele, SHK Narayanan, L Van Roekel, ... Mathematics 12 (10), 1483, 2024 | | 2024 |
A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants Y Sun, S Khairy, RB Vilim, R Hu, AJ Dave arXiv preprint arXiv:2401.13020, 2024 | | 2024 |
Introduction to Reinforcement Learning Y Sun, K Raghavan, P Balaprakash Methods and Applications of Autonomous Experimentation, 152-174, 2023 | | 2023 |